Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers.
Memory has an essential function in human life as it helps individuals remember and recognize their surroundings. It is also the major form of cognition that controls behavior. As memory is a function that is highly characteristic of humans, how it was established is of particular interest.Recent progress in the field of neurosciences, together with the technological advancement of genome-wide approaches, has led to the accumulation of evidence regarding the presence and similar/distinct mechanisms of memory among species. However, the understanding of the evolution of memory obtained utilizing these genome-wide approaches remains unclear. The purpose of this review is to provide an overview of the literature on the evolution of the memory system among species and the genes involved in this process. This review also discusses possible approaches to study the evolution of memory systems to guide future research.
The ability to record experiences and learning is present to different degrees in several species; however, the complexity and diversity of memory processes are cognitive function features that differentiate humans from other species. Lactate has recently been discovered to act as a signaling molecule for neuronal plasticity linked to long-term memory. Because lactate is not only an energy substrate for neurons but also a signaling molecule for plasticity (Magistretti and Allaman in Nat Rev Neurosci 19:235–249, 2018. https://doi.org/10.1038/nrn.2018.19), it is of particular interest to understand how and when memory-related genes and lactate-mediated neural plasticity (LMNP) genes emerged and evolved in humans. To understand the evolutionary origin and processes of memory and LMNP genes, we first collected information on genes related to memory and LMNP from the literature and then conducted a comparative analysis of these genes. We found that the memory and LMNP genes have different origins, suggesting that these genes may have become established gradually in evolutionarily and functional terms and not at the same time. We also found that memory and LMNP systems have a similar evolutionary history, having been formed with the gradual participation of newly emerging genes throughout their evolution. We propose that the function of LMNP as a signaling process may be evolutionarily associated with memory systems through an unidentified system that is linked by 13 common genes between memory and LMNP gene sets. This study provides evolutionary insight into the possible relationship between memory and the LMNP systems that deepens our understanding of the evolution of memory systems.
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